CN117148406B - Indoor and outdoor seamless elastic fusion positioning method, system, medium and equipment - Google Patents
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- G—PHYSICS
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/46—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being of a radio-wave signal type
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
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- H—ELECTRICITY
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- H—ELECTRICITY
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- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
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Abstract
The invention relates to the technical field of positioning, and discloses an indoor and outdoor seamless elastic fusion positioning method, a system, a medium and equipment, wherein the method comprises the following steps: calculating to obtain a measured error state variable at the current moment based on the equivalent time delay deviation of the tag, and predicting to obtain an error state variable at the next moment by combining an inertial navigation machine arrangement result and a state propagation model; calculating inspection information in a line-of-sight environment based on the difference value between the measured current time error state variable and the predicted current time error state variable, and adaptively adjusting a measuring noise equivalent covariance matrix by multiple factors; based on the measurement noise equivalent covariance matrix, updating the ionosphere-free observed value and the UWB ranging information after time delay compensation respectively, updating the error state variable at the current moment, and correcting the inertial navigation mechanical arrangement result to obtain a positioning result. The position information estimation performance is improved.
Description
Technical Field
The invention belongs to the technical field of positioning, and particularly relates to an indoor and outdoor seamless elastic fusion positioning method, system, medium and equipment.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Along with the continuous promotion of the construction of modern society smart cities and the service demands of navigation position, indoor and outdoor seamless navigation positioning is greatly focused by people.
In terrestrial scene multisource sensor fusion navigation, global navigation satellite systems (Global Navigation Satellite System, GNSS) including beidou satellite navigation systems (BeiDou Navigation Satellite System, BDS) can provide accurate positioning and maximum range coverage for outdoor open environments. However, for urban environments, GNSS signals cannot penetrate buildings and are greatly affected by indoor environments, and in many indoor environments, an indoor positioning system with high precision, low cost and easy installation is still a difficult problem and challenge for indoor and outdoor seamless navigation positioning. The inertial navigation system (Inertial Navigation System, INS) can obtain information such as position, attitude and speed by integrating data acquired by inertial devices such as an accelerometer and a gyroscope, but is limited by a device process and an integral calculation principle, and INS positioning accuracy is continuously reduced along with time, so that the independent INS is difficult to meet the long-time high-accuracy navigation positioning requirement. Ultra Wide Band (UWB) has the advantages of Wide bandwidth, narrow pulse and high time resolution, mainly reflected in the time arrival based measurement. Therefore, UWB positioning models based on Time of Arrival (TOA) technology have been widely studied and applied by virtue of their ranging accuracy and high technical feasibility, and the decimeter-level positioning accuracy is basically realized. However, there are still a number of problems and disadvantages to the complex indoor environment, such as: because of the environmental factors such as the material, the quantity and the material of the shielding objects on the signal propagation path, the situation that the positioning accuracy is affected by serious multipath effect, non-line-of-sight propagation and the like is very likely to exist; in addition, the positioning accuracy and the positioning service range are also greatly related to the number and the positions of UWB base stations. Compared with Bluetooth, wi-Fi and the like, the UWB serving as an indoor absolute positioning sensor has the advantages of high precision, strong penetrating power, high transmission efficiency and the like, is an important means for providing position service in urban complex environments, and solves one of the problems that the influence of non-line-of-sight errors on positioning precision is needed to be solved.
The existing method can provide certain navigation positioning capability when facing indoor and outdoor seamless environments, but various problems exist. With the remarkable improvement of satellite precision orbit and precision clock bias precision, the current precision single point positioning (Precise Point Positioning, PPP) technology has become a relatively effective method for providing all-weather and all-day position service, and the multi-system GNSS PPP has a faster convergence speed and higher precision than the GPS PPP, however, the PPP technology has difficulty in providing high-precision position service under the conditions of fewer visible numbers of GNSS satellites and poor constellation geometry. INS technology can provide short-time, high-accuracy position estimation, but because of its accumulated error, positioning accuracy diverges over time and generally cannot provide location services alone. Some researches combine GNSS and INS to improve the robustness of positioning in urban complex environments, the GNSS receiver can greatly improve the signal capturing and tracking performances under the assistance of the INS, the INS can realize the periodic correction of errors of navigation devices under the assistance of the GNSS, and the advantages of the GNSS and the INS are complementary, so that the positioning performance higher than that of independent systems of the GNSS receiver is realized. The GNSS/INS loose combination directly fuses the position information of the GNSS and the INS, is widely applied due to the simple structure, but when the subsystem cannot work, the provided position service is discontinuous; the GNSS/INS tight combination is fusion on the observation information layer, and can work normally when the number of GNSS visible satellites is less than 4, but when the GNSS satellites are interrupted for a long time, sparse GNSS observation is difficult to correct INS accumulated errors, and particularly a low-precision micromechanical inertial device.
Based on the GNSS RTK (Real-time kinematic), UWB and INS fusion indoor and outdoor seamless positioning technology of federal filtering, the inertial navigation error state vector is measured and updated by using the observation information of GNSS and UWB, the tight combination of the observation value layers is realized in the sub-filters, and finally the information of each sub-filter is fused in the main filter and dynamic information distribution is carried out according to the quality of the observation value, so that indoor and outdoor continuous seamless positioning is realized; based on the GNSS PPP technology and the UWB positioning technology, the GNSS PPP ionosphere-free combined observation equation and the UWB TOA observation equation are combined, under the conditions that the number of GNSS visible satellites is small and the observation geometry is poor, UWB serving as an external sensor can be well supplemented into the observation equation, fusion on the GNSS and UWB observation layers is realized, and position service can be provided when the number of GNSS satellites is less than 4.
Although the above researches are directed to the research of the multisource fusion indoor and outdoor seamless positioning method, the estimation performance of the position information is lower due to the non-line-of-sight error caused by the urban indoor and outdoor complex environments, and the influence of the time delay deviation on the positioning accuracy is larger.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides an indoor and outdoor seamless elastic fusion positioning method, an indoor and outdoor seamless elastic fusion positioning system, a medium and a device, wherein GNSS, UWB, INS are fused on the observation value level and the estimation performance of position information is improved by adaptively adjusting a measurement noise equivalent covariance matrix through multiple factors aiming at non-line-of-sight errors caused by urban indoor and outdoor complex environments.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the first aspect of the invention provides an indoor and outdoor seamless elastic fusion positioning method, which comprises the following steps:
acquiring a GNSS observation value, and acquiring the ionosphere-free observation value through ionosphere-free combination;
acquiring UWB ranging information, performing time delay compensation to obtain time delay compensated UWB ranging information, and calculating equivalent time delay deviation of the tag;
acquiring angular speed and acceleration measured by the INS, and arranging the inertial navigation machinery to obtain an inertial navigation machinery arranging result;
calculating to obtain a measured error state variable at the current moment based on the equivalent time delay deviation of the tag, and predicting to obtain an error state variable at the next moment by combining an inertial navigation machine arrangement result and a state propagation model;
calculating inspection information in a line-of-sight environment based on the difference value between the measured current time error state variable and the predicted current time error state variable, and adaptively adjusting a measurement noise equivalent covariance matrix by multiple factors;
based on the measurement noise equivalent covariance matrix, updating the ionosphere-free observed value and the UWB ranging information after time delay compensation respectively, updating the error state variable at the current moment, and correcting the inertial navigation mechanical arrangement result to obtain a positioning result.
Further, the tag equivalent delay deviation satisfies:
wherein:for the tag equivalent delay deviation, +.>；/>The UWB ranging information; />For base stationsiDelay deviation relative to the master base station;r i for base stationsiGeometric distance from the tag; />Is noise.
Further, the delay deviation of the base station relative to the main base station is the difference between the delay deviation of the base station and the delay deviation of the main base station.
Further, the time delay compensated UWB ranging information is obtained by subtracting a time delay deviation of the base station with respect to the main base station from the UWB ranging information.
Further, the inertial navigation mechanical orchestration result comprises an inertial navigation attitude, an inertial navigation speed and an inertial navigation position.
Further, the state propagation model is:
wherein:krepresent the firstkThe number of discrete times is one,for state transition matrix>Is discretized time process noise.
Further, the error state variables include: position error, speed error, attitude error, gyroscope zero bias error, accelerometer zero bias error and equivalent delay deviation of the tag.
A second aspect of the present invention provides an indoor and outdoor seamless elastic fusion positioning system, comprising:
a GNSS module configured to: acquiring a GNSS observation value, and acquiring the ionosphere-free observation value through ionosphere-free combination;
a UWB module configured to: acquiring UWB ranging information, performing time delay compensation to obtain time delay compensated UWB ranging information, and calculating equivalent time delay deviation of the tag;
an INS module configured to: acquiring angular speed and acceleration measured by the INS, and arranging the inertial navigation machinery to obtain an inertial navigation machinery arranging result;
a first fusion module configured to: calculating to obtain a measured error state variable at the current moment based on the equivalent time delay deviation of the tag, and predicting to obtain an error state variable at the next moment by combining an inertial navigation machine arrangement result and a state propagation model;
a second fusion module configured to: calculating inspection information in a line-of-sight environment based on the difference value between the measured current time error state variable and the predicted current time error state variable, and adaptively adjusting a measurement noise equivalent covariance matrix by multiple factors;
a third fusion module configured to: based on the measurement noise equivalent covariance matrix, updating the ionosphere-free observed value and the UWB ranging information after time delay compensation respectively, updating the error state variable at the current moment, and correcting the inertial navigation mechanical arrangement result to obtain a positioning result.
A third aspect of the present invention provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of an indoor and outdoor seamless elastic fusion positioning method as described above.
A fourth aspect of the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in an indoor and outdoor seamless elastic fusion positioning method as described above when the program is executed.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides an indoor and outdoor seamless elastic fusion positioning method, which combines GNSS, UWB and INS, realizes tight combination of observation value layers, can also be used for resolving under the condition that one or more sensors have insufficient observation information, and aims at non-line-of-sight errors caused by urban indoor and outdoor complex environments, and the estimation performance of position information is improved by adaptively adjusting a measurement noise equivalent covariance matrix through multiple factors.
The invention provides an indoor and outdoor seamless elastic fusion positioning method, which is characterized in that based on an UWB positioning model, a UWB time synchronization station is established, a base station with a stable UWB clock is used as a main base station, time delay deviation of the base station relative to the main base station is calibrated in advance by utilizing time difference observation information of the time synchronization station and the UWB base station, UWB ranging information is compensated, a resolving parameter comprises label end position information and equivalent time delay deviation, and the influence of the time delay deviation on positioning precision is fully considered.
The invention provides an indoor and outdoor seamless elastic fusion positioning method, which utilizes a centralized Kalman filter to fuse GNSS, UWB, INS three in an observation value layer, utilizes an IAE algorithm to realize elastic selection in complex environments such as indoor and outdoor environments, effectively digs complementarity of different sensors, enhances the effectiveness of the fusion positioning algorithm, and improves the precision and reliability of the position service in the complex environments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
Fig. 1 is a flowchart of an indoor and outdoor seamless elastic fusion positioning method according to a first embodiment of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
Example 1
In order to solve the vulnerability of a single sensor and realize accurate and continuous indoor and outdoor navigation positioning performance, the embodiment provides an indoor and outdoor seamless elastic fusion positioning method, which combines GNSS, UWB and INS by adopting centralized Kalman filtering, realizes tight combination of observation value layers and fully considers UWB time delay deviation (UWB label time delay deviation)cδt r And UWB base station delay deviationcδt b ) Is a function of the positioning performance of the mobile station,the indoor and outdoor seamless elastic fusion positioning is realized by utilizing a new information self-adaptive estimation (Innovation Based Adaptive Estimation, IAE) algorithm and through multi-factor self-adaptive adjustment of the weight information of non-line-of-sight abnormal observation contained in the original observation information, and belongs to the field of integrated navigation of land scenes; the influence of UWB time delay deviation on positioning performance is effectively weakened, and continuous and reliable position service can be provided under the conditions that most satellites lose lock and non-line-of-sight errors exist in complex scenes such as indoor and outdoor scenes.
Although the existing research is focused on the research of a multisource fusion indoor and outdoor seamless positioning method, how to effectively mine the complementarity of different sensors, enhance the effectiveness of a fusion positioning algorithm, and improve the precision and reliability of the position service in a complex environment is still to be studied deeply.
According to the indoor and outdoor seamless elastic fusion positioning method provided by the embodiment, based on a UWB positioning model, a UWB time synchronization station is established, a base station with a stable UWB clock is used as a main base station, time delay deviation of the base station relative to the main base station is calibrated in advance by using time difference observation information of the time synchronization station and the UWB base station, UWB ranging information is compensated, a resolving parameter comprises tag end position information and equivalent time delay deviation, and the influence of the time delay deviation on positioning precision is fully considered; the three sensors are fused, positioned and resolved by adopting a centralized Kalman filter, and the distances predicted by GNSS (Global navigation satellite System) and UWB (ultra Wide band) original observation information and INS (inertial navigation system) are used as measurement inputs of the centralized Kalman filter, so that the calculation can be performed under the condition that one or more sensors are insufficient in observation information; aiming at non-line-of-sight errors caused by urban indoor and outdoor complex environments, based on an IAE algorithm, the ranging information weight of the original observation information containing the non-line-of-sight errors is adaptively adjusted through multiple factors, so that the position information estimation performance is improved.
According to the indoor and outdoor seamless elastic fusion positioning method provided by the embodiment, GNSS, UWB, INS are fused on the observation value level by utilizing a centralized Kalman filter; the INS error equation is used as time update of the filter, when GNSS observation at a new moment is obtained, the GNSS ionosphere-free observation value is used as a measurement value to be added into a measurement update part of the filter, and when UWB observation at the new moment is obtained, UWB ranging information subjected to time delay compensation is added into the measurement update part of the filter; when GNSS or UWB original observation information contains non-line-of-sight abnormality, constructing multi-factor self-adaptive adjustment weight information containing non-line-of-sight abnormality observation in the original observation information through an IAE algorithm; after time updating and measurement updating, the system position, speed and attitude errors and zero offset errors of a gyroscope and an accelerometer are obtained, and feedback correction is carried out on the inertial navigation system, so that a final navigation result is obtained.
The indoor and outdoor seamless elastic fusion positioning method provided by the embodiment, as shown in fig. 1, comprises the following steps:
and step 1, acquiring a GNSS observation value, and obtaining the ionosphere-free observation value through ionosphere-free combination.
In this embodiment, the GNSS observations employ pseudorange observations and carrier phase observations.
The main principle of GNSS single-point positioning is that the distance is intersected at the rear, and the algorithm model function expression is as follows:
(1)
wherein:Pfor the pseudorange observations,cthe speed of light is indicated as being the speed of light,Las a result of the phase observations,for the geometrical distance between the receiver and the satellite, < >>And->Receiver clock skew and satellite clock skew,Tin order for the tropospheric delay to be sufficient,Ifor ionospheric delay, +.>And->Respectively connectReceiver-side code bias and satellite-side code bias, < >>And->The phase deviation at the receiver end and the phase deviation at the satellite end are respectively,Nfor integer ambiguity, +.>And->Pseudo-range observation noise and carrier observation noise, respectively.
The ionosphere-free combination model eliminates ionosphere parameters by utilizing the combination of signals with different frequencies, and a positioning equation is formed again, so that the ionosphere-free combination model is the most common combination mode of the double-frequency and triple-frequency signals at present. Ionospheric delays of different frequencies have the following relationship:
(2)
wherein,I i is thatL i Ionospheric delay of the frequency GNSS signals;I j is thatL j Ionospheric delay of the frequency GNSS signals;is thatL i The frequency magnitude of the frequency GNSS signal; />Is thatL j The frequency of the frequency GNSS signal.
According to formula (2), it is possible to defineL 1 AndL 2 ionosphere-free mathematical model of frequency:
(3)
wherein:and->Ionosphere-free observations combined by GNSS pseudo-range and carrier observation are respectively obtained;P 1 is thatL 1 Frequency GNSS pseudorange observations;P 2 is thatL 2 Frequency GNSS pseudorange observations;L 1 is thatL 1 Frequency GNSS carrier observations;L 2 is thatL 2 Frequency GNSS carrier observations.
Substituting formula (3) into formula (1) yields ionospheric-free observations:
(4)
wherein:，/>，/>they are all considered as one overall parameter estimate in the floating ambiguity solution; />Is the ionosphere-free receiver end code deviation;is free of ionosphere satellite end code deviation; />Is the phase deviation of the ionosphere-free receiver end; />The phase deviation of the ionosphere-free satellite end is avoided; />Is ionosphere-free whole-cycle ambiguityA degree; />The pseudo-range observation noise is ionosphere-free; />Noise is observed for ionospheric-free carriers.
And step 2, obtaining UWB observation, and performing UWB time delay compensation to obtain UWB ranging information after time delay compensation.
In this embodiment, UWB ranging information, i.e., a distance observation value between the base station and the tag, is used for UWB observation.
Aiming at the problem of time delay deviation in UWB positioning, a base station with a stable clock in UWB positioning is used as a main base station, a time synchronization station is established, and the time synchronization station is any tag capable of receiving UWB base station signals at the same time. Using time synchronization station time difference observationsInformation about UWB ranging>Compensating and compensating the UWB tag equivalent delay deviation +.>As a parameter to be solved, to weaken the influence of time delay deviation in UWB positioning on positioning performance.
The UWB TOA observation equation can be modeled as:
(5)
wherein:for UWB base stationiA distance observation from the tag;r i for base stationsiGeometric distance from the tag;cδt r is the delay deviation of the label;cδt b for base stationsiIs a delay deviation of (1); />Other noise.
Assuming base station 0 as the master base station, the UWB Time Difference (TDOA) based observation equation can be modeled as:
(6)
wherein:for base stationsiA time difference observation value with the master base station 0;r 0i for base stationsiThe time difference geometry distance from the master base station 0; />For base stationsiDelay deviation with respect to the master base station 0>，/>Delay deviation for the master base station 0;other noise.
Due to the base stationiDelay deviation from the master base station 0The time delay deviation can be calibrated in advance or broadcast in real time by establishing a time synchronization station, and the time delay deviation can be distributed in the positioning process>When the known value is substituted into equation (5), the UWB TOA observation equation is:
(7)
wherein:for the tag equivalent delay deviation, +.>。
Using base stationsiDistance observations from tagsAnd base stationiDelay deviation from the master base station 0Obtaining UWB ranging information after time delay compensation>。
And 3, acquiring angular velocity and acceleration obtained by a gyroscope and an accelerometer in INS (inertial navigation), and performing inertial navigation mechanical arrangement, wherein the mechanical arrangement respectively comprises an attitude (inertial navigation attitude matrix), an inertial navigation velocity and an inertial navigation position update, so as to obtain an inertial navigation mechanical arrangement result (comprising updated inertial navigation attitude, inertial navigation velocity and inertial navigation position).
The E-N-U geographic coordinate system (station center coordinate system) is selected as the navigation coordinate system of the system, namelynIs tied up. To be used fornThe pose, speed and position differential equations which are the references are respectively:
(8)
(9)
(10)
wherein:is a carrier systembSystem) relative to the navigation coordinate systemnTie) posture matrix,/->Is thatbIs relative tonAngular velocity of the system>Representing an antisymmetric matrix>Specific force measured for accelerometer, +.>For the self-transmitted angular velocity of the earth, < >>For linking angular velocity, the navigation system is rotated by the earth's curvature, +.>Is under the action of gravity accelerationnA tethered projection; />Is thatnTying down inertial navigation speed; />Is thatnTying a down inertial navigation position; />Is thatnIs the differentiation of the inertial navigation speed.
(1) And updating the gesture. The inertial navigation attitude update is directly obtained by adopting an attitude array chain multiplication and disassembly method according to an attitude differential equation (8):
(11)
wherein:and->Respectively ist m-1 Andt m moment inertial navigation attitudeMatrix (S)>And->Respectively byiIs used as a reference to be made to the reference,ntying and connectingbFrom the familyt m From moment to momentt m-1 A rotation matrix of time; />Is thatt m Time of daybIs tied toiRotation matrix of the system->Is thatt m Time of dayiIs tied tonRotation matrix of the system->Is thatt m-1 Time of dayiIs tied tonA rotation matrix,Is thatt m-1 Time of daybIs tied toiIs a rotation matrix.
Assuming that the gyroscope is att m-1 From moment to momentt m The time is sampled twice at equal intervals (the angular velocity is sampled), and the angular increment is respectivelyAnd->The equivalent rotation vector differential equation discretization is carried out by adopting a bispeck compensation algorithm, and the method comprises the following steps:
(12)
(13)
wherein:is a functional relationship between the direction cosine matrix and the equivalent rotation vector.
During the integration period In general, it is considered that +.A. caused by rotation and drag angular velocity of the earth>The change is small, and can be regarded as constant calculation, and the following are:
(14)
(15)
(2) And (5) updating the speed. And (3) integrating two sides of the velocity differential equation (9) simultaneously to obtain:
(16)
wherein:and->Respectively bynThe acceleration increment and the harmful acceleration increment are referred;and->Respectively->And->Inertial navigation speed at time.
And (3) shifting the term of the formula (16) to obtain a recurrence form of the inertial navigation speed updating algorithm, wherein the recurrence form is as follows:
(17)
compensation using a binary hypothesisRotation of the intermediate angular velocity and the specific force vector cannot exchange errors, and the following results:
(18)
wherein: the second term on the right of the equation is the navigation system rotation correction, and the third term on the right of the equation is the rotation effect compensation and the pitch effect compensation respectively;sampling interval +.>；/>Representation->Is relative to->Rotation of the system; />Sampling specific force velocity increments for the accelerometer; />Sampling angle increment for the gyroscope; />Sampling specific force velocity increments for the accelerometer; />Sampling a specific force velocity increment for a second equally spaced accelerometer; />Specific force velocity increments are sampled for the first equally spaced accelerometer.
During the integration periodThe rotation of the navigation system and the change of the gravity vector caused by the inside are small, < >>Can be regarded as a slow amount varying with time, and the midpoint time of the integration interval can be used>The approximate calculation is performed with:
(19)
(3) And (5) updating the position. The inertial navigation position update obtains the position parameter of the inertial navigation by integrating a position differential equation (10) and discretizing. Assuming an integration periodThe internal inertial navigation speed changes linearly along with time, and the inertial navigation position can be obtained by trapezoidal integration:
(20)
wherein,is thatt m-1 Inertial navigation at momentnThe position of the tie; />Is thatt m-1 Inertial navigation at momentnThe speed of the train; />Is thatt m Inertial navigation at momentnAnd (3) the speed of the system.
Step 4, calculating and obtaining a measured current time error state variable based on the label equivalent time delay deviation, carrying out state updating through a state propagation model by combining an inertial navigation machine arrangement result, and predicting to obtain a next time error state variableAnd takes this as the time update part of the EKF.
The GNSS/UWB/INS fusion of this embodiment adopts Errors (error) (including, position error, speed error, attitude error, zero offset error between gyroscope and accelerometer) and tag equivalent delay bias as system state variables, the dimension is 16×1, that is, the measured current time error state variables are:
(21)
wherein:is thatnThe systematic lower position error, the dimension is 3 x 1; />Is thatnThe systematic lower speed error, the dimension is 3 x 1; />The dimension is 3 multiplied by 1 as an attitude error; />Zero offset error of the gyroscope is realized, and the dimension is 3 multiplied by 1; />The dimension is 3 multiplied by 1 for the zero offset error of the accelerometer; />For GNSS receiver clock errors or UWB tag equivalent delay deviations.
The system state variable error differential equation is:
(22)
wherein:Fas an error differential equation system matrix, as shown in a formula (23), the dimension is 16×16;wprocess noise in the corresponding state as shown in formula (24), in whichIndicating that accelerometer is measuring white noise, < >>Indicating that the gyroscope measures white noise,and->Zero bias noise of the gyroscope and the accelerometer are respectively represented;Gis a process noise transfer matrix, as shown in equation (25).
(23)
(24)
(25)
Wherein,is the specific force of the accelerometer; />A 3 x 3 unit array; />Sampling intervals for gyroscopes; />Sampling intervals for the accelerometer;wfor process noise->A zero matrix of 1 x 3; />Indicating that accelerometer is measuring white noise, < >>Indicating that the gyroscope measures white noise, < >>And->Zero bias noise for the gyroscope and accelerometer, respectively.
In order to facilitate the use of discrete time Kalman filtering, a system state variable error differential equation is discretized, and a discrete time system state equation, namely a state propagation model, is constructed:
(26)
wherein:krepresent the firstkThe number of discrete times is one,is a discrete time system state transition matrix whenFAt->The time variation is not severe, and can be simplified to +.>，/>Is a discrete time interval; />Is discretized time process noise.
The discrete time state noise covariance matrix can be simplified into a trapezoidal integral:
(27)
wherein:qfor IMU sensor power spectral density,is->Covariance corresponding to process noise. Based on->A system predictive covariance matrix can be calculated>。
And 5, an IAE-based multi-factor self-adaptive elastic model. When the GNSS or UWB observed value detects that the non-line-of-sight error exists, the GNSS and UWB measurement covariance is elastically adjusted based on the IAE algorithm, so that a more robust combined positioning result is obtained.
Because the non-line-of-sight distance error is related to factors such as the number, the material, the thickness, the geometric shape and the like of obstacles in a propagation channel, an effective method for accurately estimating the magnitude of the non-line-of-sight error is lacking at present, and although the influence of the non-line-of-sight error on a positioning result can be weakened by adding IMU information, if covariance is kept unchanged all the time, the IMU effect is lower and lower along with the increase of the duration of the non-line-of-sight error until the non-line-of-sight error deflects the result of INS calculation. The IAE algorithm is applied to GNSS/UWB/INS positioning, the weight of each piece of observation information is adaptively adjusted according to the reliability of the observation information, and the influence that a reliable observation value can lose the use efficiency and the due control is not obtained for the observation value with larger error is avoided.
Construction verification information：
(28)
Wherein:the new vector of the system is tightly combined, and is the difference value between measurement information (measured current time error state variable) and prediction information (predicted current time error state variable); />For the system innovation vector covariance matrix,，/>covariance matrix is predicted for the system.
Construction of equivalent weights：
(29)
Wherein:c 1、 c 2 is a multi-factor matrix inspection threshold value, which is obtained by inspecting information in a visual range environmentThe value of the (2) is obtained regularly.
Order the，/>Is thatkThe equivalent multi-factor matrix of the time observation value is that the system measurement noise equivalent covariance matrix is:
(30)
wherein:is thatkThe time system measures the noise equivalent covariance matrix and takes it as +.>。
Step 6, after multi-factor self-adaption, inputting the measured noise equivalent covariance, the ionosphere-free observed value obtained in step 1 and the time delay compensated UWB ranging information obtained in step 2 into a measurement updating part of EKF, and updating an error state vector。
The GNSS/UWB/INS fusion EKF measurement update equation of the embodiment is as follows:
(31)
wherein:z k is measurement information;is a measurement matrix; />To measure noise, the Gaussian distribution is matched, and the covariance matrix is equivalent to the measured noise +.>Obtained.
The measurement update is divided into GNSS/INS measurement update and UWB/INS measurement update according to different conditions of the current epoch observation value.
(1) GNSS/INS measurements are updated. The GNSS/INS metrology update section may be expressed as:
(32)
wherein:the GNSS measurement information is updated ionosphere-free observation values;m GNSS ionosphere-free observations for GNSS; />Arranging ionosphere-free observation of result back calculation for the INS inertial navigation machine;IFrepresenting ionosphere-free combinations; />And->IF carrier phase and IF pseudorange measurements, respectively, i.eP IF AndL IF ；ρ IF,INS the IF geometry predicted for INS;M w as a wet mapping function；λ s,f Is wavelength; />Is the sum of the IF carrier phase and the receiver clock-dependent error correction; />The sum of the IF pseudo-range and the receiver clock-related error correction; />Is the sum of other error corrections of the IF carrier phase; />Other error fixes being IF pseudorangesPositive sum.
Measurement update coefficient array of GNSS/INS combinationThe expression is as follows:
(33)
(34)
(35)
(36)
(37)
wherein:is an IF apparent-distance jacobian matrix; />A transformation matrix of position disturbance errors from a navigation system to a geocentric earth fixed coordinate system; />The value is given by formula (13):
(38)
(39)
(2) UWB/INS measurement updates. Deducing the central position of the UWB tag antenna according to INS navigation results and lever arm measured values, wherein the central position is as follows:
(40)
wherein:calculating a position for the INS; />To tie the carrierbDown coordinate transfer to navigation systemnA lower rotation matrix; />Is a lever arm measurement; />Position errors caused by time dyssynchrony of UWB and IMU.
The approximate distance between the UWB tag antenna center position and the UWB base station position derived from INS navigation results and lever arm measurements can be expressed as:
(41)
wherein:、/>and->Derived separately for INS navigation resultse、nAnd (3) withuThree-direction UWB tag antenna center position, +.>、/>And->Base station i respectivelye、nAnd (3) withuA position in the three directions.
The UWB/INS measurement update portion may be expressed as:
(42)
wherein:the UWB measurement information is updated UWB ranging information; />And (5) the time delay compensated UWB ranging information.
Measurement update coefficient array of UWB/INS combinationThe expression is as follows:
（43）
and 7, after time updating and measurement updating, obtaining an updated current time error state variable, obtaining system position, speed and attitude errors and zero offset errors of a gyroscope and an accelerometer, and correcting an inertial navigation mechanical arrangement result so as to obtain a final navigation result (positioning result).
The simulation and measured data comparison analysis show that: compared with the traditional method, the method of the embodiment can effectively weaken the influence of UWB time delay deviation and non-line-of-sight error on indoor and outdoor seamless navigation positioning, and realize continuous and reliable indoor and outdoor position estimation.
Example two
The embodiment provides an indoor and outdoor seamless elastic fusion positioning system, which specifically comprises:
a GNSS module configured to: acquiring a GNSS observation value, and acquiring the ionosphere-free observation value through ionosphere-free combination;
a UWB module configured to: acquiring UWB ranging information, performing time delay compensation to obtain time delay compensated UWB ranging information, and calculating equivalent time delay deviation of the tag;
an INS module configured to: acquiring angular speed and acceleration measured by the INS, and arranging the inertial navigation machinery to obtain an inertial navigation machinery arranging result;
a first fusion module configured to: calculating to obtain a measured error state variable at the current moment based on the equivalent time delay deviation of the tag, and predicting to obtain an error state variable at the next moment by combining an inertial navigation machine arrangement result and a state propagation model;
a second fusion module configured to: calculating inspection information in a line-of-sight environment based on the difference value between the measured current time error state variable and the predicted current time error state variable, and adaptively adjusting a measurement noise equivalent covariance matrix by multiple factors;
a third fusion module configured to: based on the measurement noise equivalent covariance matrix, updating the ionosphere-free observed value and the UWB ranging information after time delay compensation respectively, updating the error state variable at the current moment, and correcting the inertial navigation mechanical arrangement result to obtain a positioning result.
It should be noted that, each module in the embodiment corresponds to each step in the first embodiment one to one, and the implementation process is the same, which is not described here.
Example III
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps in an indoor and outdoor seamless elastic fusion positioning method as described in the above embodiment.
Example IV
The embodiment provides a computer device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps in the indoor and outdoor seamless elastic fusion positioning method according to the embodiment.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. An indoor and outdoor seamless elastic fusion positioning method is characterized by comprising the following steps:
acquiring a GNSS observation value, and acquiring the ionosphere-free observation value through ionosphere-free combination;
acquiring UWB ranging information, performing time delay compensation to obtain time delay compensated UWB ranging information, and calculating equivalent time delay deviation of the tag;
acquiring angular speed and acceleration measured by the INS, and arranging the inertial navigation machinery to obtain an inertial navigation machinery arranging result;
calculating to obtain a measured error state variable at the current moment based on the equivalent time delay deviation of the tag, and predicting to obtain an error state variable at the next moment by combining an inertial navigation machine arrangement result and a state propagation model; the tag equivalent delay deviation satisfies:
wherein:for the tag equivalent delay deviation, +.>，cδt r For the delay deviation of the tag, +.>Delay deviation for the master base station 0; />The UWB ranging information; />For base stationsiDelay deviation relative to the master base station;r i for base stationsiGeometric distance from the tag; />Is noise;
the state propagation model is:
wherein:krepresent the firstkThe number of discrete times is one,δx k as an error variable for the kth discrete time,δx k-1 is the error variable of the kth-1 discrete time,for state transition matrix>Is discretized time process noise;
calculating inspection information in a line-of-sight environment based on the difference value between the measured current time error state variable and the predicted current time error state variable, and adaptively adjusting a measurement noise equivalent covariance matrix by multiple factors;
based on the measurement noise equivalent covariance matrix, updating the ionosphere-free observed value and the UWB ranging information after time delay compensation respectively, updating the error state variable at the current moment, and correcting the inertial navigation mechanical arrangement result to obtain a positioning result.
2. The indoor and outdoor seamless elastic fusion positioning method according to claim 1, wherein the delay deviation of the base station relative to the main base station is a difference between the delay deviation of the base station and the delay deviation of the main base station.
3. The indoor and outdoor seamless elastic fusion positioning method according to claim 1, wherein the time delay compensated UWB ranging information is obtained by subtracting a time delay deviation of a base station relative to a main base station from UWB ranging information.
4. The indoor and outdoor seamless elastic fusion positioning method according to claim 1, wherein the inertial navigation mechanical arrangement result comprises an inertial navigation attitude, an inertial navigation speed and an inertial navigation position.
5. The indoor and outdoor seamless elastic fusion positioning method according to claim 1, wherein the error state variables comprise: position error, speed error, attitude error, gyroscope zero bias error, accelerometer zero bias error and equivalent delay deviation of the tag.
6. An indoor and outdoor seamless elastic fusion positioning system, which is characterized by comprising:
a GNSS module configured to: acquiring GNSS observation, and obtaining an ionosphere-free observation value through ionosphere-free combination;
a UWB module configured to: acquiring UWB ranging information, performing time delay compensation to obtain time delay compensated UWB ranging information, and calculating equivalent time delay deviation of the tag;
an INS module configured to: acquiring angular speed and acceleration measured by the INS, and arranging the inertial navigation machinery to obtain an inertial navigation machinery arranging result;
a first fusion module configured to: calculating to obtain a measured error state variable at the current moment based on the equivalent time delay deviation of the tag, and predicting to obtain an error state variable at the next moment by combining an inertial navigation machine arrangement result and a state propagation model; the tag equivalent delay deviation satisfies:
wherein:for the tag equivalent delay deviation, +.>，cδt r For the delay deviation of the tag, +.>Delay deviation for the master base station 0; />The UWB ranging information; />For base stationsiDelay deviation relative to the master base station;r i for base stationsiGeometric distance from the tag; />Is noise;
the state propagation model is:
wherein:krepresent the firstkThe number of discrete times is one,δx k as an error variable for the kth discrete time,δx k-1 is the error variable of the kth-1 discrete time,for state transition matrix>Is discretized time process noise;
a second fusion module configured to: calculating inspection information in a line-of-sight environment based on the difference value between the measured current time error state variable and the predicted current time error state variable, and adaptively adjusting a measurement noise equivalent covariance matrix by multiple factors;
a third fusion module configured to: based on the measurement noise equivalent covariance matrix, updating the ionosphere-free observed value and the UWB ranging information after time delay compensation respectively, updating the error state variable at the current moment, and correcting the inertial navigation mechanical arrangement result to obtain a positioning result.
7. A computer readable storage medium, on which a computer program is stored, which program is executed by a processor, characterized in that the program, when executed by the processor, implements the steps of a method for indoor and outdoor seamless elastic fusion positioning according to any one of claims 1-5.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of a seamless elastic fusion positioning method according to any of claims 1-5 when the program is executed.
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